Sangraha360: An Unknown Malware Detection Framework with Federated Learning and Drift Detection
Keywords:
Malware Detection, Federated learning, Concept Drift, Drift Detection, Data Collection, Machine LearningAbstract
Strong detection mechanisms are required due to the growing threat that malware poses to the security and integrity of digital systems. To improve malware detection systems, this research study investigates the relationship between Drift Detection and Federated Learning, with an emphasis on Android devices. The heterogeneity of the Android ecosystem, its vulnerability to different kinds of malware, and the ever-changing landscape of cyber threats pose formidable obstacles for researchers. The suggested method addresses the evolving strategies of malware by integrating drift detection to monitor real-time changes in data patterns. A decentralized paradigm called federated learning is applied to cooperative model training across various Android devices while maintaining user privacy. In this study, we introduce a framework where federated learning is used in a malware identification model for the first time, and it is strategically combined with Drift detection Algorithms
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